An Industrial-Scale System for Heterogeneous Information Card Ranking in Alipay

  • Zhiqiang ZhangEmail author
  • Chaochao ChenEmail author
  • Jun ZhouEmail author
  • Xiaolong LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)


Alipay (, one of the world’s largest mobile and online payment platforms, provides not only payment services but also business about many aspects of our daily lives (finance, insurance, credit, express, news, social contact, etc.). The homepage in Alipay app ( integrates massive heterogeneous information cards, which need to be ranked in appropriate order for better user experience. This paper demonstrates an industrial-scale system for heterogeneous information card ranking. We implement an ensemble ranking model, blending online and chunked-based learning algorithms which are developed on parameter server mechanism and able to handle industrial-scale data. Moreover, we propose efficient and effective factor embedding methods, which aim to reduce high-dimensional heterogenous factor features to low-dimensional embedding vectors by subtly revealing feature interactions. Offline experimental as well as online A/B testing results illustrate the efficiency and effectiveness of our proposals.


Ranking system Industrial application Embedding 


  1. 1.
    Backstrom, L.: News Feed FYI: A Window Into News Feed. (2013). Accessed 01 05 2017
  2. 2.
    Chen, M.: Efficient vector representation for documents through corruption. In: ICLR 2017 (2017)Google Scholar
  3. 3.
    Covington, P., Adams, J., Sargin, E.: Deep neural networks for Youtube recommendations. In: RecSys 2016, pp. 191–198 (2016)Google Scholar
  4. 4.
    Gomez-Uribe, C.A., Hunt, N.: The Netflix recommender system algorithms, business value, and innovation. ACM Trans. Manag. Inf. Syst. (TMIS) 6, 13 (2016)Google Scholar
  5. 5.
    He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., Shi, Y., Atallah, A., Herbrich, R., Bowers, S., Candela, J.Q.: Practical lessons from predicting clicks on ads at facebook. In: ADKDD 2014, pp. 1–9 (2014)Google Scholar
  6. 6.
    Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: SIGKDD 2008, pp. 426–434 (2008)Google Scholar
  8. 8.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML 2014, pp. 1188–1196 (2014)Google Scholar
  9. 9.
    Lee, J., Kim, S., Lebanon, G., Singer, Y., Bengio, S.: LLORMA: local low-rank matrix approximation. J. Mach. Learn. Res. (JMLR) 17(15), 1–24 (2016)MathSciNetzbMATHGoogle Scholar
  10. 10.
    McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D., et al.: Ad click prediction: a view from the trenches. In: SIGKDD 2013, pp. 1222–1230. ACM (2013)Google Scholar
  11. 11.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS 2013, pp. 3111–3119 (2013)Google Scholar
  12. 12.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS 2007), pp. 1257–1264 (2008)Google Scholar
  13. 13.
    Shokouhi, M., Guo, Q.: From queries to cards: re-ranking proactive card recommendations based on reactive search history. In: SIGIR 2015, pp. 695–704 (2015)Google Scholar
  14. 14.
    Song, Y., Guo, Q.: Query-less: predicting task repetition for nextgen proactive search and recommendation engines. In: WWW 2016, pp. 543–553 (2016)Google Scholar
  15. 15.
    Tulloch, A.: Fast Randomized SVD (2014). Accessed 01 05 2017
  16. 16.
    Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: SIGKDD 2015, pp. 1235–1244 (2015)Google Scholar
  17. 17.
    Zhou, J., Cui, Q., Li, X., Zhao, P., Qu, S., Huang, J.: PSMART: parameter server based multiple additive regression trees system. In: WWW 2017, pp. 879–880 (2017)Google Scholar
  18. 18.
    Zhou, J., Li, X., Zhao, P., Chen, C., Li, L., Yang, X., Cui, Q., Yu, J., Chen, X., Ding, Y., Qi, Y.A.: KunPeng: parameter server based distributed learning systems and its applications in Alibaba and ant financial. In: SIGKDD 2017 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ant Financial Services GroupHangzhouChina

Personalised recommendations